Search Results (442)

This study presents a method for deriving closed-form solutions for Lagrange multipliers in worst-case performance optimization (WCPO) beamforming. By approximating the array-received signal autocorrelation matrix as a rank-1 Hermitian matrix using the low-rank approximation theory, analytical expressions for the Lagrange multipliers are derived. The method was first developed for a single plane wave scenario and then generalized to multiplane wave cases with an autocorrelation matrix rank of N. Simulations demonstrate that the proposed Lagrange multiplier formula exhibits a performance comparable to that of the second-order cone programming (SOCP) method in terms of signal-to-interference-plus-noise ratio (SINR) and direction-of-arrival (DOA) estimation accuracy, while offering a significant reduction in computational complexity. The proposed method requires three orders of magnitude less computation time than the SOCP and has a computational efficiency similar to that of the diagonal loading (DL) technique, outperforming DL in SINR and DOA estimations. Fourier amplitude spectrum analysis revealed that the beamforming filters obtained using the proposed method and the SOCP shared frequency distribution structures similar to the ideal optimal beamformer (MVDR), whereas the DL method exhibited distinct characteristics. The proposed analytical expressions for the Lagrange multipliers provide a valuable tool for implementing robust and real-time adaptive beamforming for practical applications. Full article

As an emerging technology, Reconfigurable Intelligent Surfaces (RIS) offers an efficient communication performance optimization solution for the complex and spatially constrained environment of coal mines by effectively controlling signal-propagation paths. This study investigates the channel attenuation characteristics of a semi-circular arch coal-mine tunnel with a dual RIS reflection link. By jointly optimizing the base-station beamforming matrix and the RIS phase-shift matrix, an improved Twin Delayed Deep Deterministic Policy Gradient (TD3)-based algorithm with a Noise Fading (TD3-NF) propagation optimization scheme is proposed, effectively improving the sum rate of the coal-mine wireless communication system. Simulation results show that when the transmit power is 38 dBm, the average link rate of the system reaches 11.1 bps/Hz, representing a 29.07% improvement compared to Deep Deterministic Policy Gradient (DDPG). The average sum rate of the 8 × 8 structure RIS is 3.3 bps/Hz higher than that of the 4 × 4 structure. The research findings provide new solutions for optimizing mine communication quality and applying artificial intelligence technology in complex environments. Full article

This paper presents an innovative end-to-end framework for conformal antenna array design and beam steering in Low Earth Orbit (LEO) satellite-based IoT communication systems. We propose a multi-stage learning architecture that integrates machine learning (ML) for antenna parameter prediction with reinforcement learning (RL) for adaptive beam steering. The ML module predicts optimal geometric and material parameters for conformal antenna arrays based on mission-specific performance requirements such as frequency, gain, coverage angle, and satellite constraints with an accuracy of 99%. These predictions are then passed to a Deep Q-Network (DQN)-based offline RL model, which learns beamforming strategies to maximize gain toward dynamic ground terminals, without requiring real-time interaction. To enable this, a synthetic dataset grounded in statistical principles and a static dataset is generated using CST Studio Suite and COMSOL Multiphysics simulations, capturing the electromagnetic behavior of various conformal geometries. The results from both the machine learning and reinforcement learning models show that the predicted antenna designs and beam steering angles closely align with simulation benchmarks. Our approach demonstrates the potential of combining data-driven ensemble models with offline reinforcement learning for scalable, efficient, and autonomous antenna synthesis in resource-constrained space environments. Full article

In this paper, an energy efficiency (EE)–spectral efficiency (SE) trade-off scheme is investigated for the distributed reconfigurable intelligent surface (RIS)-assisted backscatter vehicle-to-infrastructure (V2I) communication system. Firstly, a multi-objective optimization framework balancing EE and SE is established using the linear weighting method, and the quadratic transformation is utilized to recast the optimization problem as a strictly convex problem. Secondly, an alternating optimization (AO) approach is applied to partition the original problem into two independent subproblems of the BS and RIS beamforming, which are, respectively, designed by the weighted minimization mean-square error (WMMSE) and the Riemannian conjugate gradient (RCG) algorithms. Finally, according to the trade-off factor, the power reflection coefficients of backscatter devices (BDs) are dynamically optimized with the BS beamforming vectors and RIS phase shift matrices, considering their activation requirements and the vehicle minimum quality of service (QoS). The simulation results verify the effectiveness of the proposed algorithm in simultaneously improving SE and the EE in practical V2I applications through rational optimization of the BD power reflection coefficient. Full article

This paper presents the design and experimental evaluation of a compact microstrip patch antenna and a 4 × 4 phased antenna array system tailored for Wi-Fi 6E applications, U-NII-5 band. A single inset-fed microstrip patch antenna was first optimized through full-wave simulations, achieving a resonant frequency of 5.96 GHz with a measured return loss of −17.5 dB and stable broadside radiation. Building on this element, a corporate-fed 4 × 4 array was implemented on an FR4 substrate, incorporating stepped-impedance transmission lines and λ/4 transformers to ensure equal power division and impedance matching across all ports. A 4-bit digital phase shifter, controlled by an ATmega328p microcontroller, was integrated to enable electronic beam steering. Simulated results demonstrated accurate beam control within ±28°, with directional gains above 13 dBi and minimal degradation compared to the broadside case. Over-the-air measurements validated these findings, showing main lobe steering at 0°, ±15°, +33° and −30° with peak gains between 7.8 and 11.5 dBi. The proposed design demonstrates a cost-effective and practical solution for Wi-Fi 6E phased array antennas, offering enhanced beamforming, improved spatial coverage, and reliable performance in next-generation wireless networks. Full article

Passive acoustic sensing with arrays has applications in many fields, including atmospheric monitoring of low frequency sounds (i.e., infrasound). Beamforming of array signals to gain spatial information about the signal is common, but the performance is often degraded due to limited resources (e.g., number of sensors, array size). Such sparse arrays create ambiguities due to reduced resolution and spatial aliasing. While previous work has focused on either maximizing array resolution or minimizing spatial aliasing, the current study demonstrates how evolutionary algorithms can be utilized to identify array configurations that optimize for both properties. The non-dominated sorting genetic algorithm II (NSGA-II) was used with the beamwidth and maximum sidelobe level as the fitness functions to iteratively identify a group of optimized synthesized array configurations. This group is termed a Pareto-front and is optimized such that one fitness function cannot be improved without a decrease in the other. These optimized solutions were studied for a single frequency (8 Hz) and a multi-frequency (3 to 20 Hz) signal using either a 36-element or 9-element array with a 60 m aperture. The performance of the synthesized arrays was compared against established array configurations (baseline) with most of the Pareto-front solutions outperforming these baseline configurations. The largest improvements to array performance using the synthesized configurations were with fewer array elements and the multi-frequency signal. Full article

This study tackles the issue of ensuring secure communications in vehicular ad hoc networks (VANETs) under dynamic eavesdropping threats, where eavesdroppers adaptively reposition to intercept transmissions. We introduce a scheme utilizing a partitioned reconfigurable intelligent surface (RIS) to assist in the joint transmission of confidential signals and artificial noise (AN) from a source station. The RIS is divided into segments: one enhances legitimate signal reflection toward the intended vehicular receiver, while the other directs AN toward eavesdroppers to degrade their reception. To maximize secrecy performance in rapidly changing environments, we introduce a joint optimization framework integrating meta-learning for RIS partitioning and reinforcement learning (RL) for reflection matrix optimization. The meta-learning component rapidly determines the optimal RIS partitioning ratio when encountering new eavesdropping scenarios, leveraging prior experience to adapt with minimal data. Subsequently, RL is employed to dynamically optimize both beamforming vectors as well as RIS reflection coefficients, thereby further improving the security performance. Extensive simulations demonstrate that the suggested approach attain a 28% higher secrecy rate relative to conventional RIS-assisted techniques, along with more rapid convergence compared to traditional deep learning approaches. This framework successfully balances signal enhancement with jamming interference, guaranteeing robust and energy-efficient security in highly dynamic vehicular settings. Full article

This paper addresses secure transmission in a high-occlusion urban environment, where an intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communication system serves a legitimate user while countering an eavesdropper. The UAV signal is reflected to the base station through the IRS. We study the secure energy efficiency optimization problem. The tightly coupled optimization variables make the problem difficult to solve directly. Therefore, we decompose the original problem into three sub-problems. For the UAV active beamforming design, the closed-form solution can be obtained directly. For the IRS phase shift optimization, we propose an optimization algorithm based on Riemannian manifolds to obtain the optimal solution. Due to the non-convex fractional UAV trajectory optimization, it can be solved by successive convex approximation (SCA) and the Dinkelbach algorithm. Different comparison schemes are designed to evaluate the effectiveness of the proposed algorithm. The simulation results show that the proposed algorithm has improved advantages compared with other schemes. Full article

Underwater acoustic communication faces significant challenges including limited bandwidth, high propagation delays, severe multipath fading, and stringent energy constraints. While integrated sensing and communication (ISAC) has shown promise in radio frequency systems, its adaptation to underwater environments remains challenging due to the unique acoustic channel characteristics and the inadequacy of traditional delay-based performance metrics that fail to capture the spatio-temporal value of information in dynamic underwater scenarios. This paper presents a comprehensive underwater ISAC framework centered on a novel Spatio-Temporal Information-Theoretic Freshness metric that fundamentally transforms resource allocation from delay minimization to value maximization. Unlike conventional approaches that treat all data equally, our spatio-temporal framework enables intelligent prioritization by recognizing that obstacle detection data directly ahead of an autonomous underwater vehicle (AUV) require immediate processing. Our framework addresses key underwater ISAC challenges through spatio-temporal-guided power allocation, adaptive beamforming, waveform optimization, and cooperative sensing strategies. Multi-agent reinforcement learning algorithms enable coordinated resource allocation and mission-critical information prioritization across heterogeneous networks comprising surface buoys, AUVs, and static sensors. Extensive simulations in realistic Munk profile acoustic environments demonstrate significant performance improvements. The spatio-temporal framework successfully filters spatially irrelevant data, resulting in substantial energy savings for battery-constrained underwater nodes. Full article

Driven by advancements in communication technology, the Internet of Vehicles (IoV) has gained significant importance. Its capability for real-time information exchange and processing substantially enhances data transmission performance within multi-node distributed systems. Among core physical layer transmission technologies, beamforming and power allocation are crucial for optimizing system efficiency. However, the real-time joint optimization of the transmitter, receiver, and power allocation in MIMO-based IoV systems remains insufficiently addressed in existing research. To bridge this gap, this paper proposes a framework for the real-time joint optimization of beamforming and power allocation, aiming to maximize transmission efficiency while satisfying constant modulus constraints and power limitations. The proposed framework decomposes the problem and utilizes the CVX library to obtain a local optimum for the joint scheme. The simulation results show that compared with traditional beamforming methods, this scheme has better performance in multiple indicators, increasing the transmission rate of the system by 43%, having faster convergence speed, and improving spectral efficiency. Thus, this study achieves real-time joint optimization of MIMO beamforming and power allocation for IoV scenarios, providing crucial technical support for related designs. Full article

To enhance the spectral efficiency of hybrid beamforming in millimeter-wave massive MIMO systems, the problem is formulated as a high-dimensional non-convex optimization under constant modulus constraints. A novel algorithm based on fourth-order tensor Tucker decomposition is proposed. Specifically, the frequency-domain channel matrices are structured into a fourth-order tensor to explicitly capture the couplings across the spatial, frequency, and user domains. To tackle the non-convexity induced by constant modulus constraints, the analog precoder and combiner are derived by solving a truncated-rank Tucker decomposition problem through the Alternating Direction Method of Multipliers and Alternating Least Squares schemes. Subsequently, in the digital domain, the Regularized Block Diagonalization algorithm is integrated with the subcarrier and user factor matrices—obtained from the tensor decomposition—along with the water-filling strategy to design the digital precoder and combiner, thereby achieving a balance between multi-user interference suppression and noise enhancement. The proposed tensor-based algorithm is demonstrated through simulations to outperform existing state-of-the-art schemes. This work provides an efficient and mathematically sound solution for hybrid beamforming in dense multi-user scenarios envisioned for sixth-generation mobile communications. Full article

Urban unmanned aerial vehicle (UAV) surveillance faces significant obstacles due to visual obstructions, inadequate lighting, small target dimensions, and acoustic signal interference caused by environmental noise and multipath propagation. To address these issues, this study proposes a multimodal detection framework that integrates an efficient YOLOv11-based visual detection module—trained on a comprehensive dataset containing over 50,000 UAV images—with a Capon beamforming-based acoustic imaging system using a 144-element spiral-arm microphone array. Adaptive compensation strategies are implemented to improve the robustness of each sensing modality, while detections results are validated through intersection-over-union and angular deviation metrics. The angular validation is accomplished by mapping acoustic direction-of-arrival estimations onto the camera image plane using established calibration parameters. Experimental evaluation reveals that the fusion system achieves outstanding performance under optimal conditions, exceeding 99% accuracy. However, its principal advantage becomes evident in challenging environments where individual modalities exhibit considerable limitations. The fusion approach demonstrates substantial performance improvements across three critical scenarios. In low-light conditions, the fusion system achieves 78% accuracy, significantly outperforming vision-only methods which attain only 25% accuracy. Under occlusion scenarios, the fusion system maintains 99% accuracy while vision-only performance drops dramatically to 9.75%, though acoustic-only detection remains highly effective at 99%. In multi-target detection scenarios, the fusion system reaches 96.8% accuracy, bridging the performance gap between vision-only systems at 99% and acoustic-only systems at 54%, where acoustic intensity variations limit detection capability. These experimental findings validate the effectiveness of the complementary fusion strategy and establish the system’s practical value for urban airspace monitoring applications. Full article

Terahertz (THz) communication is a key technology for sixth-generation (6G) networks, offering ultra-high data rates, low latency, and massive connectivity. However, the THz band faces significant propagation challenges, including high path loss, molecular absorption, and susceptibility to blockage. Reconfigurable intelligent surfaces (RISs) have emerged as an effective solution to overcome these limitations by reconfiguring the wireless environment through passive beam steering. In this work, we propose a novel framework, namely the optimized deep reinforcement learning transformer (ODRL-Transformer), to maximize the sum rate in RIS-assisted THz systems. The framework integrates a Transformer encoder for extracting temporal and contextual features from sequential channel observations, a DRL agent for adaptive beamforming and phase shift control, and a hybrid biogeography-based optimization (HBBO) algorithm for tuning the hyperparameters of both modules. This design enables efficient long-term decisionmaking and improved convergence. Extensive simulations of dynamic THz channel models demonstrate that ODRL-Transformer outperforms other optimization baselines in terms of the sum rate, convergence speed, stability, and generalization. The proposed model achieved an error rate of 0.03, strong robustness, and fast convergence, highlighting its potential for intelligent resource allocation in next-generation RIS-assisted THz networks. Full article

The optimization of beamforming in multi-base station (multi-BS) reconfigurable intelligent surface (RIS)-aided systems is a challenging task due to its high computational complexity. This paper first demonstrates that an optimized multi-BS system exhibits superior communication performance compared to a centralized large-scale single-BS system. To efficiently solve the complex beamforming problem in the multi-BS environment, this paper proposes a novel optimization solver based on a graph neural network (GNN) that models the physical structure of the system. Experimental results show that the proposed GNN solver finds solutions of higher quality, achieving a 42% performance increase with 45% less total computational complexity compared to a conventional iterative optimization method. Furthermore, when compared to other complex AI models such as transformer and Bi-LSTM, the proposed GNN achieves similar state-of-the-art performance while having less than 1% of the parameters and a fraction of the computational cost. These findings demonstrate that the GNN is a powerful, efficient, and practical solution for beamforming optimization in multi-BS RIS-aided systems, satisfying the demands for performance, computational efficiency, and model compactness. Full article

Vehicular communication is crucial for the future of intelligent transportation systems. However, providing continuous high-data-rate connectivity for vehicles in hard-to-reach areas, such as highways, rural regions, and disaster zones, is challenging, as deploying ground base stations (BSs) is either infeasible or too costly. In this paper, multiple unmanned aerial vehicles (UAVs) using millimeter-wave (mmWave) bands are proposed to deliver high-data-rate and secure communication links to vehicles. This is due to UAVs’ ability to fly, hover, and maneuver, and to mmWave properties of high data rate and security, enabled by beamforming capabilities. In this scenario, the vehicle should autonomously select the optimal UAV to maximize its achievable data rate and ensure long coverage periods so as to reduce the frequency of UAV handovers, while considering the UAVs’ battery lives. However, predicting UAVs’ coverage periods and optimizing mmWave beam directions are challenging, since no prior information is available about UAVs’ positions, speeds, or altitudes. To overcome this, out-of-band communication using orthogonal time-frequency space (OTFS) modulation is employed to enable the vehicle to estimate UAVs’ speeds and positions by assessing channel state information (CSI) in the Delay-Doppler (DD) domain. This information is used to predict maximum coverage periods and optimize mmWave beamforming, allowing for the best UAV selection. Compared to other benchmarks, the proposed scheme shows significant performance in various scenarios. Full article